Automated multi-scale computational pathotyping (AMSCP) of inflamed synovial tissue

Rheumatoid arthritis (RA) is a complex immune-mediated inflammatory disorder in which patients suffer from inflammatory-erosive arthritis. Recent advances on histopathology heterogeneity of RA synovial tissue revealed three distinct phenotypes based on cellular composition (pauci-immune, diffuse and lymphoid), suggesting that distinct etiologies warrant specific targeted therapy which motivates a need for cost effective phenotyping tools in preclinical and clinical settings. To this end, we developed an automated multi-scale computational pathotyping (AMSCP) pipeline for both human and mouse synovial tissue with two distinct components that can be leveraged together or independently: (1) segmentation of different tissue types to characterize tissue-level changes, and (2) cell type classification within each tissue compartment that assesses change across disease states. Here, we demonstrate the efficacy, efficiency, and robustness of the AMSCP pipeline as well as the ability to discover novel phenotypes. Taken together, we find AMSCP to be a valuable cost-effective method for both pre-clinical and clinical research.

Authors

Institutions

Publication Details

Journal
Nature Communications
Published
2024-08-29
DOI
https://doi.org/10.1038/s41467-024-51012-6
Citations
7
Primary Topic
Digital Imaging for Blood Diseases
Type
article
Field-Weighted Citation Impact
1.74

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Automated multi-scale computational pathotyping (AMSCP) of inflamed synovial tissue

Michael B. Brenner, Jason D. Turner, Kamil Slowikowski, Akiko Noma et al.
7 citations
Nature Communications
Digital Imaging for Blood Diseases
1.74
article

Automated multi-scale computational pathotyping (AMSCP) of inflamed synovial tissue

Michael B. Brenner, Jason D. Turner, Kamil Slowikowski, Akiko Noma, Arthur M. Mandelin, Anupamaa Seshadri, Paul J. Utz, William H. Robinson, Darren Tabechian, David L. Boyle, William Apruzzese, Jane H. Buckner, Karen Salomon-Escoto, Christopher T. Ritchlin, Brendan F. Boyce, Josh Keegan, Zilong Bai, Laura T. Donlin, Ellen M. Gravallese, Mandy J. McGeachy, Jennifer H. Anolik, Harris Perlman, Hayley Carr, Jack L. McMurray, Joel M. Guthridge, Fumitaka Mizoguchi, Javier Rangel‐Moreno, Daria M. Krenitsky, Dana E. Orange, James A. Lederer, V. Michael Holers, Maxwell A. Konnaris, Vivian P. Bykerk, Mark Alan Fontana, Andrew D. Filer, Nir Hacohen, Kevin Wei, María Gutiérrez‐Arcelus, Dagmar Scheel‐Toellner, Miguel Otero, Matthew Brendel, Chamith Y. Fonseka, Joseph Mears, Costantino Pitzalis, Thomas Eisenhaure, Yvonne Lee, David Lieb, H. Mark Kenney, Christopher Dominic Buckley, Larry W. Moreland, Mina Rohani‐Pichavant, Jennifer S. Albrecht, Nida Meednu, Richard D. Bell, Danielle Sutherby, Eddie A. James, Edward M. Schwarz, Anna Helena Jonsson, Saba Nayar, Gerald F. Watts, Edward F. DiCarlo, Soumya Raychaudhuri, Jennifer P. Nguyen, Deepak A. Rao, Lionel B. Ivashkiv, Judith A. James, S. Louis Bridges, Cristina Rozo, Jennifer Seifert, Fei Wang, Stephen Kelly, Chad Nusbaum, Peter K. Gregersen, Laura B. Hughes, Gary S. Firestein, Michael A. McNamara, Justin Xiang, James P. Dolan
article en
7 citations

Abstract

Rheumatoid arthritis (RA) is a complex immune-mediated inflammatory disorder in which patients suffer from inflammatory-erosive arthritis. Recent advances on histopathology heterogeneity of RA synovial tissue revealed three distinct phenotypes based on cellular composition (pauci-immune, diffuse and lymphoid), suggesting that distinct etiologies warrant specific targeted therapy which motivates a need for cost effective phenotyping tools in preclinical and clinical settings. To this end, we developed an automated multi-scale computational pathotyping (AMSCP) pipeline for both human and mouse synovial tissue with two distinct components that can be leveraged together or independently: (1) segmentation of different tissue types to characterize tissue-level changes, and (2) cell type classification within each tissue compartment that assesses change across disease states. Here, we demonstrate the efficacy, efficiency, and robustness of the AMSCP pipeline as well as the ability to discover novel phenotypes. Taken together, we find AMSCP to be a valuable cost-effective method for both pre-clinical and clinical research.

Nature CommunicationsVol. 15(1)
Broad Institute (US), Northwestern University (US), Tokyo Medical and Dental University (JP), Hospital for Special Surgery (US), Brigham and Women's Hospital (US), Northwell Health (US), Pennsylvania State University (US), University Hospitals Birmingham NHS Foundation Trust (GB), Virginia Mason Medical Center (US), Feinstein Institute for Medical Research (US), Harvard University (US), Queen Mary University of London (GB), University of Massachusetts Chan Medical School (US), University of Pittsburgh (US), Palo Alto University (US), Cornell University (US), Barts Health NHS Trust (GB), Queen Elizabeth Hospital Birmingham (GB), University of Rochester Medical Center (US), Oklahoma Medical Research Foundation (US), University of Alabama at Birmingham (US), University of California San Diego (US), Chappaqua Central School District (US), William Harvey Research Institute (GB), NIHR Birmingham Biomedical Research Centre (GB), Benaroya Research Institute, University of Birmingham (GB), Rockefeller University (US), Stanford University (US)
National Institute of Arthritis and Musculoskeletal and Skin Diseases
Openalex Percentile: Top 14%
Digital Imaging for Blood Diseases
1.74
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.